Overview

Dataset statistics

Number of variables16
Number of observations1326
Missing cells2024
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory165.9 KiB
Average record size in memory128.1 B

Variable types

Numeric13
Categorical3

Alerts

id has a high cardinality: 1326 distinct values High cardinality
name has a high cardinality: 1324 distinct values High cardinality
symbol has a high cardinality: 1301 distinct values High cardinality
Unnamed: 0 is highly correlated with rankHigh correlation
24h_volume_usd is highly correlated with market_cap_usdHigh correlation
available_supply is highly correlated with max_supply and 1 other fieldsHigh correlation
market_cap_usd is highly correlated with 24h_volume_usdHigh correlation
max_supply is highly correlated with available_supply and 1 other fieldsHigh correlation
price_btc is highly correlated with price_usdHigh correlation
price_usd is highly correlated with price_btcHigh correlation
rank is highly correlated with Unnamed: 0High correlation
total_supply is highly correlated with available_supply and 1 other fieldsHigh correlation
percent_change_24h is highly correlated with percent_change_7dHigh correlation
percent_change_7d is highly correlated with percent_change_24hHigh correlation
24h_volume_usd has 56 (4.2%) missing values Missing
available_supply has 295 (22.2%) missing values Missing
market_cap_usd has 295 (22.2%) missing values Missing
max_supply has 1111 (83.8%) missing values Missing
percent_change_1h has 53 (4.0%) missing values Missing
percent_change_24h has 56 (4.2%) missing values Missing
percent_change_7d has 43 (3.2%) missing values Missing
total_supply has 115 (8.7%) missing values Missing
24h_volume_usd is highly skewed (γ1 = 29.0769414) Skewed
available_supply is highly skewed (γ1 = 23.49169619) Skewed
market_cap_usd is highly skewed (γ1 = 29.48570252) Skewed
price_btc is highly skewed (γ1 = 34.62710239) Skewed
price_usd is highly skewed (γ1 = 34.62532428) Skewed
total_supply is highly skewed (γ1 = 34.78862922) Skewed
Unnamed: 0 is uniformly distributed Uniform
id is uniformly distributed Uniform
name is uniformly distributed Uniform
rank is uniformly distributed Uniform
symbol is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
id has unique values Unique
rank has unique values Unique

Reproduction

Analysis started2022-10-30 13:05:01.254562
Analysis finished2022-10-30 13:05:47.803089
Duration46.55 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1326
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean662.5
Minimum0
Maximum1325
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:48.032316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66.25
Q1331.25
median662.5
Q3993.75
95-th percentile1258.75
Maximum1325
Range1325
Interquartile range (IQR)662.5

Descriptive statistics

Standard deviation382.9275388
Coefficient of variation (CV)0.5780038322
Kurtosis-1.2
Mean662.5
Median Absolute Deviation (MAD)331.5
Skewness0
Sum878475
Variance146633.5
MonotonicityStrictly increasing
2022-10-30T16:05:48.201766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
8901
 
0.1%
8881
 
0.1%
8871
 
0.1%
8861
 
0.1%
8851
 
0.1%
8841
 
0.1%
8831
 
0.1%
8821
 
0.1%
8811
 
0.1%
Other values (1316)1316
99.2%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
13251
0.1%
13241
0.1%
13231
0.1%
13221
0.1%
13211
0.1%
13201
0.1%
13191
0.1%
13181
0.1%
13171
0.1%
13161
0.1%

24h_volume_usd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct1262
Distinct (%)99.4%
Missing56
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean16455930.11
Minimum0.0867857
Maximum9007640000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:48.369660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0867857
5-th percentile11.527005
Q1320.06175
median4935.38
Q3208942.25
95-th percentile9736401
Maximum9007640000
Range9007640000
Interquartile range (IQR)208622.1883

Descriptive statistics

Standard deviation274046115.1
Coefficient of variation (CV)16.65333489
Kurtosis925.7891114
Mean16455930.11
Median Absolute Deviation (MAD)4923.1898
Skewness29.0769414
Sum2.089903124 × 1010
Variance7.510127321 × 1016
MonotonicityNot monotonic
2022-10-30T16:05:48.537970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.251233
 
0.2%
12.51233
 
0.2%
87.58582
 
0.2%
2.502452
 
0.2%
37.53682
 
0.2%
2.504952
 
0.2%
23.05551
 
0.1%
3.530331
 
0.1%
68.32611
 
0.1%
65.51471
 
0.1%
Other values (1252)1252
94.4%
(Missing)56
 
4.2%
ValueCountFrequency (%)
0.08678571
 
0.1%
0.3753681
 
0.1%
0.389961
 
0.1%
0.510811
 
0.1%
1.021
 
0.1%
1.251233
0.2%
1.364211
 
0.1%
1.591561
 
0.1%
1.86521
 
0.1%
1.86721
 
0.1%
ValueCountFrequency (%)
90076400001
0.1%
29360900001
0.1%
15513300001
0.1%
11113500001
0.1%
10847900001
0.1%
5498600001
0.1%
4093420001
0.1%
4020670001
0.1%
2578380001
0.1%
2490480001
0.1%

available_supply
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct1002
Distinct (%)97.2%
Missing295
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean8657165024
Minimum0
Maximum3.209032899 × 1012
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:48.817339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile426244.5
Q15012184
median22396250
Q3111281312
95-th percentile5187355314
Maximum3.209032899 × 1012
Range3.209032899 × 1012
Interquartile range (IQR)106269128

Descriptive statistics

Standard deviation1.140542288 × 1011
Coefficient of variation (CV)13.17454715
Kurtosis622.8978029
Mean8657165024
Median Absolute Deviation (MAD)21245816
Skewness23.49169619
Sum8.92553714 × 1012
Variance1.30083671 × 1022
MonotonicityNot monotonic
2022-10-30T16:05:48.970161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000004
 
0.3%
100000004
 
0.3%
500000003
 
0.2%
10000003
 
0.2%
60000003
 
0.2%
6000000003
 
0.2%
10000000003
 
0.2%
700000003
 
0.2%
395475252
 
0.2%
3000000002
 
0.2%
Other values (992)1001
75.5%
(Missing)295
 
22.2%
ValueCountFrequency (%)
01
0.1%
11
0.1%
421
0.1%
511
0.1%
901
0.1%
3001
0.1%
10001
0.1%
17271
0.1%
20081
0.1%
47731
0.1%
ValueCountFrequency (%)
3.209032899 × 10121
0.1%
1.336622935 × 10121
0.1%
7.56097561 × 10111
0.1%
5.385574142 × 10111
0.1%
4.431681825 × 10111
0.1%
3.251902154 × 10111
0.1%
2.229487385 × 10111
0.1%
1.832535346 × 10111
0.1%
1.755284858 × 10111
0.1%
1.47896135 × 10111
0.1%

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1326
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
bitcoin
 
1
coexistcoin
 
1
tattoocoin
 
1
bitz
 
1
independent-money-system
 
1
Other values (1321)
1321 

Length

Max length31
Median length24
Mean length8.708898944
Min length2

Characters and Unicode

Total characters11548
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1326 ?
Unique (%)100.0%

Sample

1st rowbitcoin
2nd rowethereum
3rd rowbitcoin-cash
4th rowiota
5th rowripple

Common Values

ValueCountFrequency (%)
bitcoin1
 
0.1%
coexistcoin1
 
0.1%
tattoocoin1
 
0.1%
bitz1
 
0.1%
independent-money-system1
 
0.1%
prime-xi1
 
0.1%
marscoin1
 
0.1%
dreamcoin1
 
0.1%
bumbacoin1
 
0.1%
paycoin21
 
0.1%
Other values (1316)1316
99.2%

Length

2022-10-30T16:05:49.139695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bitcoin1
 
0.1%
bitcoin-cash1
 
0.1%
ripple1
 
0.1%
dash1
 
0.1%
litecoin1
 
0.1%
bitcoin-gold1
 
0.1%
monero1
 
0.1%
cardano1
 
0.1%
ethereum-classic1
 
0.1%
nem1
 
0.1%
Other values (1316)1316
99.2%

Most occurring characters

ValueCountFrequency (%)
i1227
10.6%
o1188
 
10.3%
n1128
 
9.8%
e953
 
8.3%
c908
 
7.9%
a761
 
6.6%
t722
 
6.3%
r641
 
5.6%
s529
 
4.6%
l444
 
3.8%
Other values (25)3047
26.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11201
97.0%
Dash Punctuation302
 
2.6%
Decimal Number45
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1227
11.0%
o1188
10.6%
n1128
10.1%
e953
 
8.5%
c908
 
8.1%
a761
 
6.8%
t722
 
6.4%
r641
 
5.7%
s529
 
4.7%
l444
 
4.0%
Other values (16)2700
24.1%
Decimal Number
ValueCountFrequency (%)
217
37.8%
09
20.0%
16
 
13.3%
35
 
11.1%
83
 
6.7%
72
 
4.4%
42
 
4.4%
91
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11201
97.0%
Common347
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1227
11.0%
o1188
10.6%
n1128
10.1%
e953
 
8.5%
c908
 
8.1%
a761
 
6.8%
t722
 
6.4%
r641
 
5.7%
s529
 
4.7%
l444
 
4.0%
Other values (16)2700
24.1%
Common
ValueCountFrequency (%)
-302
87.0%
217
 
4.9%
09
 
2.6%
16
 
1.7%
35
 
1.4%
83
 
0.9%
72
 
0.6%
42
 
0.6%
91
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1227
10.6%
o1188
 
10.3%
n1128
 
9.8%
e953
 
8.3%
c908
 
7.9%
a761
 
6.6%
t722
 
6.3%
r641
 
5.6%
s529
 
4.6%
l444
 
3.8%
Other values (25)3047
26.4%

last_updated
Real number (ℝ≥0)

Distinct105
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1512544914
Minimum1511625871
Maximum1512549589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:49.304720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1511625871
5-th percentile1512549541
Q11512549549
median1512549562
Q31512549575
95-th percentile1512549585
Maximum1512549589
Range923718
Interquartile range (IQR)26

Descriptive statistics

Standard deviation37930.05843
Coefficient of variation (CV)2.507697992 × 10-5
Kurtosis293.8390785
Mean1512544914
Median Absolute Deviation (MAD)13
Skewness-14.7780434
Sum2.005634556 × 1012
Variance1438689332
MonotonicityNot monotonic
2022-10-30T16:05:49.503668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151254954141
 
3.1%
151254954235
 
2.6%
151254954732
 
2.4%
151254957632
 
2.4%
151254954632
 
2.4%
151254958532
 
2.4%
151254956132
 
2.4%
151254956531
 
2.3%
151254957531
 
2.3%
151254954431
 
2.3%
Other values (95)997
75.2%
ValueCountFrequency (%)
15116258711
0.1%
15120743531
0.1%
15122231591
0.1%
15122363501
0.1%
15122567621
0.1%
15122747591
0.1%
15122909531
0.1%
15123017711
0.1%
15123188611
0.1%
15123449691
0.1%
ValueCountFrequency (%)
15125495891
 
0.1%
15125495885
 
0.4%
151254958719
1.4%
151254958624
1.8%
151254958532
2.4%
151254958426
2.0%
151254958323
1.7%
151254958222
1.7%
151254958129
2.2%
151254958030
2.3%

market_cap_usd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct1031
Distinct (%)100.0%
Missing295
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean363050308
Minimum10
Maximum2.130493467 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:49.701886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile25479.5
Q1188062.5
median1488564
Q315467560
95-th percentile210156175.5
Maximum2.130493467 × 1011
Range2.130493467 × 1011
Interquartile range (IQR)15279497.5

Descriptive statistics

Standard deviation6844947029
Coefficient of variation (CV)18.85399042
Kurtosis909.0009777
Mean363050308
Median Absolute Deviation (MAD)1458416
Skewness29.48570252
Sum3.743048675 × 1011
Variance4.685329983 × 1019
MonotonicityNot monotonic
2022-10-30T16:05:49.896652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1120191
 
0.1%
1745971
 
0.1%
1731451
 
0.1%
1647871
 
0.1%
1619311
 
0.1%
1597551
 
0.1%
1572301
 
0.1%
1565591
 
0.1%
1554741
 
0.1%
1514491
 
0.1%
Other values (1021)1021
77.0%
(Missing)295
 
22.2%
ValueCountFrequency (%)
101
0.1%
231
0.1%
1001
0.1%
1361
0.1%
2511
0.1%
5141
0.1%
5151
0.1%
8561
0.1%
21871
0.1%
22881
0.1%
ValueCountFrequency (%)
2.130493467 × 10111
0.1%
4.35294462 × 10101
0.1%
2.529585276 × 10101
0.1%
1.47522458 × 10101
0.1%
93653432631
0.1%
57940755691
0.1%
56344975281
0.1%
49200650491
0.1%
43316875421
0.1%
32314204371
0.1%

max_supply
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct125
Distinct (%)58.1%
Missing1111
Missing (%)83.8%
Infinite0
Infinite (%)0.0%
Mean4.655418237 × 1012
Minimum300
Maximum1 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:50.096872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile3220760.4
Q121500000
median100000000
Q3658577440
95-th percentile2.5 × 1010
Maximum1 × 1015
Range1 × 1015
Interquartile range (IQR)637077440

Descriptive statistics

Standard deviation6.819914467 × 1013
Coefficient of variation (CV)14.64941305
Kurtosis214.9999706
Mean4.655418237 × 1012
Median Absolute Deviation (MAD)95000000
Skewness14.6628768
Sum1.000914921 × 1015
Variance4.651123334 × 1027
MonotonicityNot monotonic
2022-10-30T16:05:50.286597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000018
 
1.4%
2100000016
 
1.2%
10000000009
 
0.7%
840000007
 
0.5%
2100000005
 
0.4%
420000004
 
0.3%
2000000004
 
0.3%
200000004
 
0.3%
40000000003
 
0.2%
20000000003
 
0.2%
Other values (115)142
 
10.7%
(Missing)1111
83.8%
ValueCountFrequency (%)
3001
0.1%
12501
0.1%
4200001
0.1%
6296101
0.1%
7229351
0.1%
10000002
0.2%
20000001
0.1%
26843191
0.1%
29411341
0.1%
30000001
0.1%
ValueCountFrequency (%)
1 × 10151
 
0.1%
1.8447 × 10111
 
0.1%
1 × 10112
0.2%
6.713 × 10101
 
0.1%
5 × 10102
0.2%
4.5 × 10101
 
0.1%
3 × 10102
0.2%
2.5 × 10102
0.2%
2.1 × 10103
0.2%
2 × 10101
 
0.1%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1324
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
Enigma
 
2
HempCoin
 
2
Bitcoin
 
1
PayCoin
 
1
Tattoocoin (Standard Edition)
 
1
Other values (1319)
1319 

Length

Max length29
Median length26
Mean length8.664404223
Min length2

Characters and Unicode

Total characters11489
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1322 ?
Unique (%)99.7%

Sample

1st rowBitcoin
2nd rowEthereum
3rd rowBitcoin Cash
4th rowIOTA
5th rowRipple

Common Values

ValueCountFrequency (%)
Enigma2
 
0.2%
HempCoin2
 
0.2%
Bitcoin1
 
0.1%
PayCoin1
 
0.1%
Tattoocoin (Standard Edition)1
 
0.1%
Bitz1
 
0.1%
Independent Money System1
 
0.1%
Prime-XI1
 
0.1%
Marscoin1
 
0.1%
Dreamcoin1
 
0.1%
Other values (1314)1314
99.1%

Length

2022-10-30T16:05:50.496660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
coin46
 
2.9%
token25
 
1.6%
bitcoin14
 
0.9%
gold11
 
0.7%
network11
 
0.7%
ethereum8
 
0.5%
cash6
 
0.4%
digital5
 
0.3%
crypto5
 
0.3%
money4
 
0.3%
Other values (1392)1456
91.5%

Most occurring characters

ValueCountFrequency (%)
i1137
 
9.9%
o1101
 
9.6%
n1036
 
9.0%
e814
 
7.1%
a629
 
5.5%
t561
 
4.9%
r544
 
4.7%
C499
 
4.3%
c402
 
3.5%
l364
 
3.2%
Other values (60)4402
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8608
74.9%
Uppercase Letter2520
 
21.9%
Space Separator267
 
2.3%
Decimal Number44
 
0.4%
Open Punctuation14
 
0.1%
Close Punctuation14
 
0.1%
Dash Punctuation11
 
0.1%
Other Punctuation10
 
0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i1137
13.2%
o1101
12.8%
n1036
12.0%
e814
9.5%
a629
 
7.3%
t561
 
6.5%
r544
 
6.3%
c402
 
4.7%
l364
 
4.2%
s356
 
4.1%
Other values (16)1664
19.3%
Uppercase Letter
ValueCountFrequency (%)
C499
19.8%
B194
 
7.7%
S172
 
6.8%
T154
 
6.1%
P140
 
5.6%
A130
 
5.2%
E128
 
5.1%
M106
 
4.2%
D104
 
4.1%
R95
 
3.8%
Other values (16)798
31.7%
Decimal Number
ValueCountFrequency (%)
213
29.5%
011
25.0%
36
13.6%
15
 
11.4%
43
 
6.8%
83
 
6.8%
72
 
4.5%
91
 
2.3%
Other Punctuation
ValueCountFrequency (%)
.8
80.0%
/1
 
10.0%
'1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
[8
57.1%
(6
42.9%
Close Punctuation
ValueCountFrequency (%)
]8
57.1%
)6
42.9%
Space Separator
ValueCountFrequency (%)
267
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11128
96.9%
Common361
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i1137
 
10.2%
o1101
 
9.9%
n1036
 
9.3%
e814
 
7.3%
a629
 
5.7%
t561
 
5.0%
r544
 
4.9%
C499
 
4.5%
c402
 
3.6%
l364
 
3.3%
Other values (42)4041
36.3%
Common
ValueCountFrequency (%)
267
74.0%
213
 
3.6%
-11
 
3.0%
011
 
3.0%
.8
 
2.2%
[8
 
2.2%
]8
 
2.2%
)6
 
1.7%
(6
 
1.7%
36
 
1.7%
Other values (8)17
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII11489
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i1137
 
9.9%
o1101
 
9.6%
n1036
 
9.0%
e814
 
7.1%
a629
 
5.5%
t561
 
4.9%
r544
 
4.7%
C499
 
4.3%
c402
 
3.5%
l364
 
3.2%
Other values (60)4402
38.3%

percent_change_1h
Real number (ℝ)

MISSING

Distinct588
Distinct (%)46.2%
Missing53
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1.052435192
Minimum-70.85
Maximum232.31
Zeros2
Zeros (%)0.2%
Negative423
Negative (%)31.9%
Memory size10.5 KiB
2022-10-30T16:05:50.734174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-70.85
5-th percentile-6.276
Q1-0.17
median0.52
Q30.97
95-th percentile8.872
Maximum232.31
Range303.16
Interquartile range (IQR)1.14

Descriptive statistics

Standard deviation12.00550133
Coefficient of variation (CV)11.40735451
Kurtosis158.9331154
Mean1.052435192
Median Absolute Deviation (MAD)0.64
Skewness9.348541089
Sum1339.75
Variance144.1320623
MonotonicityNot monotonic
2022-10-30T16:05:50.914708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54180
 
13.6%
0.5282
 
6.2%
-0.1274
 
5.6%
0.5152
 
3.9%
0.511
 
0.8%
0.5310
 
0.8%
0.497
 
0.5%
0.567
 
0.5%
0.557
 
0.5%
-0.016
 
0.5%
Other values (578)837
63.1%
(Missing)53
 
4.0%
ValueCountFrequency (%)
-70.851
0.1%
-62.81
0.1%
-49.431
0.1%
-47.131
0.1%
-39.381
0.1%
-37.551
0.1%
-37.531
0.1%
-31.521
0.1%
-31.221
0.1%
-29.291
0.1%
ValueCountFrequency (%)
232.311
0.1%
177.71
0.1%
111.381
0.1%
101.081
0.1%
90.721
0.1%
63.481
0.1%
51.61
0.1%
46.731
0.1%
45.321
0.1%
41.091
0.1%

percent_change_24h
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1114
Distinct (%)87.7%
Missing56
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean8.655677165
Minimum-95.85
Maximum833.01
Zeros2
Zeros (%)0.2%
Negative559
Negative (%)42.2%
Memory size10.5 KiB
2022-10-30T16:05:51.085937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-95.85
5-th percentile-27.151
Q1-6.6325
median1.82
Q311.6625
95-th percentile60.705
Maximum833.01
Range928.86
Interquartile range (IQR)18.295

Descriptive statistics

Standard deviation44.01074918
Coefficient of variation (CV)5.084610752
Kurtosis116.5548637
Mean8.655677165
Median Absolute Deviation (MAD)8.88
Skewness8.087789764
Sum10992.71
Variance1936.946043
MonotonicityNot monotonic
2022-10-30T16:05:51.249838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3129
 
2.2%
6.2712
 
0.9%
-3.339
 
0.7%
6.35
 
0.4%
-8.613
 
0.2%
5.13
 
0.2%
3.053
 
0.2%
-5.513
 
0.2%
1.393
 
0.2%
6.323
 
0.2%
Other values (1104)1197
90.3%
(Missing)56
 
4.2%
ValueCountFrequency (%)
-95.851
0.1%
-94.221
0.1%
-93.931
0.1%
-79.021
0.1%
-76.551
0.1%
-74.371
0.1%
-68.561
0.1%
-63.751
0.1%
-61.41
0.1%
-58.891
0.1%
ValueCountFrequency (%)
833.011
0.1%
457.861
0.1%
416.041
0.1%
295.491
0.1%
272.51
0.1%
267.21
0.1%
252.071
0.1%
244.261
0.1%
230.871
0.1%
223.311
0.1%

percent_change_7d
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1190
Distinct (%)92.8%
Missing43
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean29.53653936
Minimum-99.59
Maximum3360.71
Zeros1
Zeros (%)0.1%
Negative421
Negative (%)31.7%
Memory size10.5 KiB
2022-10-30T16:05:51.523909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-99.59
5-th percentile-40.137
Q1-7.775
median11.43
Q335.645
95-th percentile135.708
Maximum3360.71
Range3460.3
Interquartile range (IQR)43.42

Descriptive statistics

Standard deviation121.1550582
Coefficient of variation (CV)4.101870457
Kurtosis450.577975
Mean29.53653936
Median Absolute Deviation (MAD)21.31
Skewness17.39453457
Sum37895.38
Variance14678.54813
MonotonicityNot monotonic
2022-10-30T16:05:51.678379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.4410
 
0.8%
17.499
 
0.7%
17.394
 
0.3%
6.663
 
0.2%
2.073
 
0.2%
76.233
 
0.2%
-4.992
 
0.2%
-25.282
 
0.2%
27.682
 
0.2%
7.082
 
0.2%
Other values (1180)1243
93.7%
(Missing)43
 
3.2%
ValueCountFrequency (%)
-99.591
0.1%
-96.611
0.1%
-95.311
0.1%
-92.681
0.1%
-87.431
0.1%
-81.291
0.1%
-80.661
0.1%
-76.861
0.1%
-75.031
0.1%
-73.621
0.1%
ValueCountFrequency (%)
3360.711
0.1%
928.971
0.1%
693.371
0.1%
582.111
0.1%
560.461
0.1%
547.121
0.1%
545.731
0.1%
542.961
0.1%
525.731
0.1%
521.471
0.1%

price_btc
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct895
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03397066796
Minimum2 × 10-12
Maximum31.4507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:51.844522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2 × 10-12
5-th percentile1 × 10-8
Q13.7 × 10-7
median3.905 × 10-6
Q33.7125 × 10-5
95-th percentile0.001244015
Maximum31.4507
Range31.4507
Interquartile range (IQR)3.6755 × 10-5

Descriptive statistics

Standard deviation0.8793566931
Coefficient of variation (CV)25.88576398
Kurtosis1233.003181
Mean0.03397066796
Median Absolute Deviation (MAD)3.895 × 10-6
Skewness34.62710239
Sum45.04510572
Variance0.7732681937
MonotonicityNot monotonic
2022-10-30T16:05:52.011270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 × 10-856
 
4.2%
2 × 10-821
 
1.6%
3 × 10-813
 
1.0%
5 × 10-812
 
0.9%
4 × 10-811
 
0.8%
6 × 10-810
 
0.8%
7 × 10-810
 
0.8%
1 × 10-710
 
0.8%
1.7 × 10-79
 
0.7%
9 × 10-89
 
0.7%
Other values (885)1165
87.9%
ValueCountFrequency (%)
2 × 10-121
 
0.1%
1 × 10-112
 
0.2%
2 × 10-111
 
0.1%
3 × 10-111
 
0.1%
4 × 10-111
 
0.1%
8 × 10-113
0.2%
1 × 10-101
 
0.1%
2 × 10-105
0.4%
3 × 10-101
 
0.1%
5 × 10-101
 
0.1%
ValueCountFrequency (%)
31.45071
0.1%
51
0.1%
2.086331
0.1%
2.044911
0.1%
1.137321
0.1%
11
0.1%
0.9856571
0.1%
0.3058511
0.1%
0.161
0.1%
0.120051
0.1%

price_usd
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1220
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425.2208167
Minimum2.79 × 10-8
Maximum393520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:52.172658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.79 × 10-8
5-th percentile0.00012466825
Q10.00462954
median0.0488604
Q30.461517
95-th percentile15.565425
Maximum393520
Range393520
Interquartile range (IQR)0.45688746

Descriptive statistics

Standard deviation11002.94757
Coefficient of variation (CV)25.87584413
Kurtosis1232.916
Mean425.2208167
Median Absolute Deviation (MAD)0.048735277
Skewness34.62532428
Sum563842.803
Variance121064855.3
MonotonicityNot monotonic
2022-10-30T16:05:52.335568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00012512324
 
1.8%
0.00037536810
 
0.8%
0.0002502457
 
0.5%
0.000500495
 
0.4%
0.0007507355
 
0.4%
0.002127085
 
0.4%
0.001251234
 
0.3%
0.003378314
 
0.3%
0.001751724
 
0.3%
0.003753684
 
0.3%
Other values (1210)1254
94.6%
ValueCountFrequency (%)
2.79 × 10-81
0.1%
1.346 × 10-71
0.1%
1.493 × 10-71
0.1%
2.037 × 10-71
0.1%
3.269 × 10-71
0.1%
4.499 × 10-71
0.1%
9.458 × 10-71
0.1%
9.832 × 10-71
0.1%
1.0216 × 10-61
0.1%
1.3498 × 10-61
0.1%
ValueCountFrequency (%)
3935201
0.1%
62561.31
0.1%
26104.71
0.1%
25586.41
0.1%
14230.41
0.1%
12739.51
0.1%
12332.81
0.1%
3826.891
0.1%
2001.961
0.1%
1502.091
0.1%

rank
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1326
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean663.5
Minimum1
Maximum1326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:52.520669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile67.25
Q1332.25
median663.5
Q3994.75
95-th percentile1259.75
Maximum1326
Range1325
Interquartile range (IQR)662.5

Descriptive statistics

Standard deviation382.9275388
Coefficient of variation (CV)0.5771326885
Kurtosis-1.2
Mean663.5
Median Absolute Deviation (MAD)331.5
Skewness0
Sum879801
Variance146633.5
MonotonicityStrictly increasing
2022-10-30T16:05:52.739805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
8911
 
0.1%
8891
 
0.1%
8881
 
0.1%
8871
 
0.1%
8861
 
0.1%
8851
 
0.1%
8841
 
0.1%
8831
 
0.1%
8821
 
0.1%
Other values (1316)1316
99.2%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
13261
0.1%
13251
0.1%
13241
0.1%
13231
0.1%
13221
0.1%
13211
0.1%
13201
0.1%
13191
0.1%
13181
0.1%
13171
0.1%

symbol
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1301
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
QBT
 
2
HNC
 
2
ACC
 
2
KNC
 
2
BTM
 
2
Other values (1296)
1316 

Length

Max length9
Median length3
Mean length3.523378582
Min length1

Characters and Unicode

Total characters4672
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1276 ?
Unique (%)96.2%

Sample

1st rowBTC
2nd rowETH
3rd rowBCH
4th rowMIOTA
5th rowXRP

Common Values

ValueCountFrequency (%)
QBT2
 
0.2%
HNC2
 
0.2%
ACC2
 
0.2%
KNC2
 
0.2%
BTM2
 
0.2%
MNC2
 
0.2%
ICN2
 
0.2%
MGC2
 
0.2%
RMC2
 
0.2%
CMT2
 
0.2%
Other values (1291)1306
98.5%

Length

2022-10-30T16:05:52.954816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qbt2
 
0.2%
arc2
 
0.2%
hnc2
 
0.2%
xid2
 
0.2%
btcs2
 
0.2%
bet2
 
0.2%
ebtc2
 
0.2%
cash2
 
0.2%
cat2
 
0.2%
btg2
 
0.2%
Other values (1291)1306
98.5%

Most occurring characters

ValueCountFrequency (%)
C427
 
9.1%
T374
 
8.0%
R286
 
6.1%
S286
 
6.1%
A282
 
6.0%
N264
 
5.7%
E263
 
5.6%
B242
 
5.2%
O228
 
4.9%
I220
 
4.7%
Other values (29)1800
38.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4608
98.6%
Decimal Number59
 
1.3%
Currency Symbol3
 
0.1%
Other Punctuation1
 
< 0.1%
Lowercase Letter1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C427
 
9.3%
T374
 
8.1%
R286
 
6.2%
S286
 
6.2%
A282
 
6.1%
N264
 
5.7%
E263
 
5.7%
B242
 
5.3%
O228
 
4.9%
I220
 
4.8%
Other values (16)1736
37.7%
Decimal Number
ValueCountFrequency (%)
220
33.9%
08
 
13.6%
18
 
13.6%
37
 
11.9%
86
 
10.2%
43
 
5.1%
73
 
5.1%
62
 
3.4%
91
 
1.7%
51
 
1.7%
Currency Symbol
ValueCountFrequency (%)
$3
100.0%
Other Punctuation
ValueCountFrequency (%)
@1
100.0%
Lowercase Letter
ValueCountFrequency (%)
c1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4609
98.7%
Common63
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C427
 
9.3%
T374
 
8.1%
R286
 
6.2%
S286
 
6.2%
A282
 
6.1%
N264
 
5.7%
E263
 
5.7%
B242
 
5.3%
O228
 
4.9%
I220
 
4.8%
Other values (17)1737
37.7%
Common
ValueCountFrequency (%)
220
31.7%
08
 
12.7%
18
 
12.7%
37
 
11.1%
86
 
9.5%
43
 
4.8%
73
 
4.8%
$3
 
4.8%
62
 
3.2%
@1
 
1.6%
Other values (2)2
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C427
 
9.1%
T374
 
8.0%
R286
 
6.1%
S286
 
6.1%
A282
 
6.0%
N264
 
5.7%
E263
 
5.6%
B242
 
5.2%
O228
 
4.9%
I220
 
4.7%
Other values (29)1800
38.5%

total_supply
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED

Distinct1024
Distinct (%)84.6%
Missing115
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean7.849644695 × 1011
Minimum1
Maximum9.223424444 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2022-10-30T16:05:53.184756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile785966
Q18768462.5
median42337077
Q3254489796.5
95-th percentile1.469420947 × 1010
Maximum9.223424444 × 1014
Range9.223424444 × 1014
Interquartile range (IQR)245721334

Descriptive statistics

Standard deviation2.650661422 × 1013
Coefficient of variation (CV)33.76791594
Kurtosis1210.49553
Mean7.849644695 × 1011
Median Absolute Deviation (MAD)40940633
Skewness34.78862922
Sum9.505919726 × 1014
Variance7.026005976 × 1026
MonotonicityNot monotonic
2022-10-30T16:05:53.405517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000000044
 
3.3%
100000000032
 
2.4%
1000000015
 
1.1%
2100000010
 
0.8%
500000009
 
0.7%
1 × 10108
 
0.6%
10000008
 
0.6%
2000000007
 
0.5%
200000007
 
0.5%
1 × 10116
 
0.5%
Other values (1014)1065
80.3%
(Missing)115
 
8.7%
ValueCountFrequency (%)
12
0.2%
421
0.1%
511
0.1%
901
0.1%
3001
0.1%
12501
0.1%
21571
0.1%
25081
0.1%
47731
0.1%
88201
0.1%
ValueCountFrequency (%)
9.223424444 × 10141
0.1%
1 × 10131
0.1%
8 × 10121
0.1%
3.209032899 × 10121
0.1%
1.336622935 × 10121
0.1%
6.10229997 × 10111
0.1%
5.385574142 × 10111
0.1%
4.431681825 × 10111
0.1%
2.33728949 × 10111
0.1%
2.229487385 × 10111
0.1%

Interactions

2022-10-30T16:05:44.427173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-30T16:05:53.851303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-30T16:05:54.192361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-30T16:05:54.400204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-30T16:05:47.722841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 024h_volume_usdavailable_supplyidlast_updatedmarket_cap_usdmax_supplynamepercent_change_1hpercent_change_24hpercent_change_7dprice_btcprice_usdranksymboltotal_supply
009.007640e+091.672352e+07bitcoin15125495542.130493e+112.100000e+07Bitcoin0.127.3317.451.00000012739.5000001BTC1.672352e+07
111.551330e+099.616537e+07ethereum15125495534.352945e+10NaNEthereum-0.18-3.93-7.330.036177452.6520002ETH9.616537e+07
221.111350e+091.684044e+07bitcoin-cash15125495782.529585e+102.100000e+07Bitcoin Cash1.65-5.51-4.750.1200501502.0900003BCH1.684044e+07
332.936090e+092.779530e+09iota15125495711.475225e+102.779530e+09IOTA-2.3883.35255.820.0004245.3074604MIOTA2.779530e+09
442.315050e+083.873915e+10ripple15125495419.365343e+091.000000e+11Ripple0.56-3.70-14.790.0000190.2417545XRP9.999309e+10
552.289430e+087.736420e+06dash15125495425.794076e+091.890000e+07Dash1.22-3.3110.640.059856748.9350006DASH7.736420e+06
664.093420e+085.415391e+07litecoin15125495425.634498e+098.400000e+07Litecoin-0.170.803.680.008316104.0460007LTC5.415391e+07
771.384070e+081.669097e+07bitcoin-gold15125495824.920065e+092.100000e+07Bitcoin Gold-0.86-8.65-11.240.023559294.7740008BTG1.679097e+07
885.498600e+081.544296e+07monero15125495444.331688e+09NaNMonero-2.0025.6541.230.022418280.4960009XMR1.544296e+07
996.164750e+072.592707e+10cardano15125495793.231420e+094.500000e+10Cardano-0.28-5.80-8.250.0000100.12463510ADA3.111248e+10

Last rows

Unnamed: 024h_volume_usdavailable_supplyidlast_updatedmarket_cap_usdmax_supplynamepercent_change_1hpercent_change_24hpercent_change_7dprice_btcprice_usdranksymboltotal_supply
13161316NaNNaNteracoin1511625871NaNNaNTeraCoinNaNNaNNaN1.000000e-080.0000861317TERA9.223372e+10
13171317NaNNaNzsecoin1512533665NaNNaNZSEcoinNaN3.85-11.449.200000e-070.0112491318ZSE6.768156e+06
13181318NaNNaNbixc1512256762NaNNaNBIXCNaNNaN0.994.007800e-044.4119701319BIXC2.100000e+08
13191319NaNNaNtyrocoin1512485969NaNNaNTyrocoinNaN-79.02-87.431.500000e-070.0017701320TYC5.062930e+07
13201320NaNNaNpicoin1512545967NaN2941134.0PiCoin-0.099.79NaN2.200000e-070.0027381321PI1.378369e+06
13211321NaNNaNturbocoin1512368664NaNNaNTurboCoinNaNNaN8.121.000000e-080.0001141322TURBONaN
13221322NaNNaNbirds1512535772NaNNaNBirdsNaN10.62-42.101.000000e-080.0001221323BIRDSNaN
13231323NaNNaNbitcoincashscrypt1512548078NaNNaNBitcoinCashScrypt-0.37-37.39-27.695.000000e-070.0062021324BCCS2.502380e+06
13241324NaNNaNswisscoin1512540278NaNNaNSwisscoinNaN4.39-22.841.000000e-080.0001231325SIC1.020000e+10
13251325NaNNaNfaceblock1512435283NaNNaNFaceblockNaNNaN-6.831.400000e-070.0016541326FBL1.000000e+07